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Record W2265567801 · doi:10.2172/881510

Ecological Screening Values for Surface Water, Sediment, and Soil: 2005 Update

2005· report· en· W2265567801 on OpenAlexaboutno aff
G.P. Friday

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsnot available
FundersSavannah River National LaboratoryU.S. Fish and Wildlife ServiceOak Ridge National LaboratoryNational Oceanic and Atmospheric AdministrationU.S. Department of EnergyU.S. Environmental Protection AgencyRijksinstituut voor Volksgezondheid en MilieuU.S. Geological Survey
KeywordsEnvironmental remediationEnvironmental scienceRemedial actionRisk assessmentNuclear decommissioningEnvironmental impact assessmentBaseline (sea)WildlifeEcological assessmentEnvironmental resource managementEnvironmental planningEcologyEngineeringContaminationWaste managementFisheryComputer science

Abstract

fetched live from OpenAlex

One of the principal components of the environmental remediation program at the Savannah River Site (SRS) is the assessment of ecological risk. Used to support CERCLA, RCRA, and DOE orders, the ecological risk assessment (ERA) can identify environmental hazards and evaluate remedial action alternatives. Ecological risk assessment is also an essential means for achieving DOE's risk based end state vision for the disposition of nuclear material and waste hazards, the decommissioning of facilities, and the remediation of inactive waste units at SRS. The complexity of an ERA ranges from a screening level ERA (SLERA) to a full baseline ERA. A screening level ecological risk assessments, although abbreviated from a baseline risk assessment, is nonetheless considered a complete risk assessment (EPA, 2001a). One of the initial tasks of any ERA is to identify constituents that potentially or adversely affect the environment. Typically, this is accomplished by comparing a constituent's maximum concentration in surface water, sediment, or soil with an ecological screening value (ESV). The screening process can eliminate many constituents from further consideration in the risk assessment, but it also identifies those that require additional evaluation. This document is an update of a previous compilation (Friday, 1998) and provides a comprehensive listing of ecological screening values for surface water, sediment, and soil. It describes how the screening values were derived and recommends benchmarks that can be used for ecological risk assessment. The sources of these updated benchmarks include the U.S. Environmental Protection Agency (EPA), U.S. Fish and Wildlife Service (USFWS), U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), Oak Ridge National Laboratory (ORNL), the State of Florida, the Canadian Council of Ministers of the Environment (CCME), the Dutch Ministry of the Environment (RIVM), and the scientific literature. It should be noted that ESV's are continuously revised by the various issuing agencies. The references in this report provide the citations of each source and, where applicable, the internet address where they can be accessed. Although radiological screening values are not included herein due to space limitations, these have been recently derived by a technical working committee sponsored by the U.S. Department of Energy (DOE 2002, 2004). The recommended ecological screening values represent the most conservative concentrations of the cited sources, and are to be used for screening purposes only. They do not represent remedial action cleanup levels. Their use at locations other than SRS should take into account environmental variables such as water quality, soil chemistry, flora and fauna, and other ecological attributes specific to the ecosystem potentially at risk.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.007
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.272
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2005
Admission routes1
Has abstractyes

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